Accelerated Simplified Swarm Optimization with Exploitation Search Scheme for Data Clustering

PloS One
Wei-Chang Yeh, Chyh-Ming Lai

Abstract

Data clustering is commonly employed in many disciplines. The aim of clustering is to partition a set of data into clusters, in which objects within the same cluster are similar and dissimilar to other objects that belong to different clusters. Over the past decade, the evolutionary algorithm has been commonly used to solve clustering problems. This study presents a novel algorithm based on simplified swarm optimization, an emerging population-based stochastic optimization approach with the advantages of simplicity, efficiency, and flexibility. This approach combines variable vibrating search (VVS) and rapid centralized strategy (RCS) in dealing with clustering problem. VVS is an exploitation search scheme that can refine the quality of solutions by searching the extreme points nearby the global best position. RCS is developed to accelerate the convergence rate of the algorithm by using the arithmetic average. To empirically evaluate the performance of the proposed algorithm, experiments are examined using 12 benchmark datasets, and corresponding results are compared with recent works. Results of statistical analysis indicate that the proposed algorithm is competitive in terms of the quality of solutions.

References

Jun 9, 2005·IEEE Transactions on Neural Networks·Rui Xu, Donald Wunsch
Feb 7, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·K Krishna, M Narasimha Murty
Jan 1, 1984·IEEE Transactions on Pattern Analysis and Machine Intelligence·S Z Selim, M A Ismail

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Software Mentioned

ELS
RCS
OFK
VSSO
gBest
BH
KMO
MATLAB
KM
KSRPSO

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